To build linear probes, we first obtained parallel SSL model and baseline features using the 27 fMRI stimulus stories. For baseline features with a lower sampling rate (spectrotemporal, phonemes, words), SSL layer representations were mean-pooled across time. To predict FBANK and spectrotemporal features, we used ridge regression to map each SSL model layer to each feature. Probing performance was measured by the correlation between the predicted and true feature value across the test set, averaged across all features in each space. For phoneme and word identity we trained linear classifiers and measured performance with classification accuracy and perplexity, respectively. We used 100-D GloVe embeddings [1]} and the associated vocabulary for the word-level tasks. To ensure that these results were not biased by the limited capabilities of linear probes, we repeated some analyses using linear MLPs with a single bottleneck layer, and found no difference (Appendix ). Each linear probe was trained and evaluated on 3 different seeds. For each run, we randomly divided the 27 stories into an 80-10-10 train-validation-test split.
